Meta Releases Llama 4 Herd with 32 Models Totaling 70 Trillion Parameters
Meta open-sources the Llama 4 family, comprising 32 distinct models ranging from 1 billion to 8 trillion parameters each, for a cumulative 70 trillion parameters trained on 120 trillion tokens. The release includes mixture-of-experts variants with 405 billion active parameters that outperform GPT-5.2 on 18 of 22 public benchmarks while running inference at 40 percent lower cost. All weights, training logs, and 15,000-line system prompts publish under an unrestricted commercial license effective immediately.
Llama 4 Herd introduces native multimodal reasoning across text, image, video, and audio with a unified 4-million-token context window. The flagship Llama 4-405B-MoE achieves 94.2 percent on MMLU-Pro, 91.8 percent on GPQA Diamond, and 89.7 percent on SWE-Bench Verified, surpassing closed-source leaders while consuming only 405 billion active parameters per forward pass. Training required 3.8 million H100-equivalent GPU hours on Meta’s newly commissioned 600-megawatt Oregon cluster.
Meta Releases Llama 4 Herd with 32 Models Totaling 70 Trillion Parameters
Meta open-sources the Llama 4 family, comprising 32 distinct models ranging from 1 billion to 8 trillion parameters each, for a cumulative 70 trillion parameters trained on 120 trillion tokens. The release includes mixture-of-experts variants with 405 billion active parameters that outperform GPT-5.2 on 18 of 22 public benchmarks while running inference at 40 percent lower cost. All weights, training logs, and 15,000-line system prompts publish under an unrestricted commercial license effective immediately.
Llama 4 Herd introduces native multimodal reasoning across text, image, video, and audio with a unified 4-million-token context window. The flagship Llama 4-405B-MoE achieves 94.2 percent on MMLU-Pro, 91.8 percent on GPQA Diamond, and 89.7 percent on SWE-Bench Verified, surpassing closed-source leaders while consuming only 405 billion active parameters per forward pass. Training required 3.8 million H100-equivalent GPU hours on Meta’s newly commissioned 600-megawatt Oregon cluster.
Smaller models include distilled 8B and 70B versions optimized for on-device deployment, running at 120 tokens per second on Apple M4 Pro chips and 85 tokens per second on Snapdragon 8 Elite phones. These edge variants retain 87 percent of the herd’s reasoning capability through knowledge distillation and quantization-aware training. Meta ships inference binaries for iOS, Android, Windows on Arm, and Linux with 4-bit integer support out of the box.
The training dataset expands to 120 trillion tokens scraped through December 10, 2025, including full-resolution YouTube-8M captions, 40 billion scientific PDFs, and licensed content from Springer Nature and IEEE. Data decontamination pipelines remove all benchmark leakage, verified by third-party auditors at Epoch AI who confirm zero contamination across 50 evaluation sets.
Meta publishes the entire training configuration, including learning rate schedules, ZeRO-3 offload parameters, and FlashAttention-3 kernel tweaks that delivered 61 percent MFU on H200 hardware. The company also releases a 100-billion-parameter routing dataset enabling developers to train custom MoE gating networks for domain adaptation. First-party fine-tunes for medical diagnostics and legal reasoning accompany the base models.
Safety mitigations embed constitutional classifiers directly into the transformer blocks, achieving 99.3 percent refusal rate on adversarial prompts while maintaining 0.8 percent false positives on benign queries. Meta implements gradient-based red-teaming throughout training, exposing the herd to 2.1 million harmful prompts before release. Independent evaluations by GLUECoS and Anthropic confirm the system poses low risk under current deployment guidelines.
Downloads exceed 8 million in the first six hours across Hugging Face and Meta’s own mirrors. Cloud providers including AWS, Azure, and Google Cloud activate one-click deployment buttons charging $0.42 per million input tokens and $1.68 per million output tokens for the 405B-MoE, undercutting OpenAI’s GPT-5.2 Pro by 60 percent. On-premises pricing for the full herd requires 128 H100 GPUs with NVLink for 2.1 tokens per second at FP8 precision.
Mark Zuckerberg states the release fulfills Meta’s commitment to open innovation, noting that 65 percent of production AI workloads at the company already run on prior Llama models. The herd architecture allows dynamic routing reduces inference carbon footprint by 72 percent versus dense equivalents through selective activation. Meta plans quarterly updates with Llama 4.1 scheduled for March 2026 targeting 200 trillion cumulative parameters.
